Any product will eventually fail, regardless of how well it is engineered. Often failure can be attributed to structural, material, or manufacturing defects, even for electronic products. A failure at the component or sub-component level often results in failure of the overall system. For example, cracking of a piston rod can result in failure of a car, and loss of a solder joint can result in failure of an electronic component. Such failures present safety or maintenance concerns and often result in loss of market share.
A way to predict the impending failure of a system or component would be useful to allow operators to repair or retire the component or system before the actual failure, and thus avoid negative consequences associated from an actual failure.
Accurate prediction of impending structural, mechanical, or system failure could have great economic impact to industries within the aerospace, automotive, electronics, medical device, appliance and related sectors.
Engineers currently attempt to design products for high reliability. But it is most often the case that reliability information comes very late in the design process. Often a statistically significant amount of reliability data is not obtained until after product launch and warranty claims from use by consumers. This lack of data makes it common for engineers to add robustness to their designs by using safety factors to ensure that a design meets reliability goals.
Safety factors, however, are subjective in nature and usually based on historical use. Since modern manufacturers are incorporating new technology and manufacturing methods faster than ever before, exactly what safety factor is appropriate to today's new complex, state-of-the-art product is seldom, if ever, known with certainty. This complicates the engineering process. In addition, safety factors tend to add material or structural components or add complexity to the manufacturing process. They are counterproductive where industry is attempting to cut cost or reduce weight. Designing cost effective and highly reliable structures therefore requires the ability to reduce the safety factor as much as possible for a given design.
In attempting to reduce reliance on safety factors, designers have, over the years, developed models for the damage mechanisms that lead to failures. Failures can be attributed to many different kinds of damage mechanisms such as fatigue, buckling, and corrosion. These models are used during the design process, usually through deterministic analysis, to identify feasible design concept alternatives. But poor or less than desired reliability is often attributed to variability, and deterministic analysis fails to account for variability.
Variability affects product reliability through any number of factors including loading scenarios, environmental condition changes, usage patterns, and maintenance habits. Even a system response to a steady input can exhibit variability, such as a steady flow pipe with varying degrees of corrosion.
Historically, testing has been the means for evaluating effects of variability. Unfortunately, testing is a slow, expensive process and evaluation of every possible source of variability is not practical.
Over the years, probabilistic techniques have been developed for predicting variability and have been coupled with damage models of failure mechanisms to provide probabilistic damage models that predict the reliability of a population. But, given variability, a prediction of the reliability of a population says little about the future life of an individual member of the population. Safety factors are likewise unsatisfactory methods for predicting the life of an individual since they are based on historical information obtained from a population. Safety factors are also an unsatisfactory method for quickly and efficiently designing against failure since they rely on historical information obtained from test and component data. As a result, there exists a need for a method and apparatus for accurately predicting component and/or system failure that accounts for variability without the need for extensive test data on the component and/or system.